MCP is The Protocol Running Your AI Strategy

Your engineering team is already using Model Context Protocol. Right now. They’re connecting Claude, ChatGPT, and other AI agents to your Slack, databases, CRMs, and internal APIs through it.

Ask them about it in your next 1:1. Watch them assume you already know what MCP is.

The Protocol Built for Agents, Not Humans

MCP is the first major protocol designed for AI agents rather than human users or traditional software. That’s the fundamental problem executives are missing.

Traditional software follows deterministic paths. Input A produces Output B, every time. You secure the known pathways, lock down the predictable access patterns, and sleep reasonably well.

AI agents improvise. They interpret ambiguous instructions, find creative solutions you never intended, and occasionally hallucinate entire workflows. They don’t follow your flowcharts—they write their own in real-time based on natural language requests that can mean twelve different things depending on context.

This creates attack surfaces we’ve never managed before.

The Security Nightmare You’re Not Discussing

MCP assumes benign actors. The protocol was built for utility, not security. Now we’re retrofitting permissions and access controls onto infrastructure that’s already in production.

Consider what your AI agents can access right now:

• Read every Slack channel (including the sensitive ones)
• Write to production databases
• Call authenticated APIs
• Access customer data
• Execute financial transactions

All based on natural language instructions they will inevitably misinterpret.

One vague prompt. One hallucinated command. One creative interpretation of “update all the records.” You’re not equipped to audit AI decision-making the way you audit human decisions or traditional software execution.

The Painful Education Currently In Progress

Every organization implementing MCP is running live experiments on critical infrastructure. There’s no playbook because this protocol is fundamentally new. We’re all learning what can go wrong by watching it go wrong.

Your competitors are experiencing this too. They’re just not talking about it yet.

The executives who understand MCP today will be the ones whose organizations survive the next wave of AI integration. The ones flying blind will learn through expensive, public failures.

What You Actually Need

You need guardrails. Not theoretical ones—deployed guardrails that understand agent behavior patterns, enforce boundaries AI agents will actually respect, and log the improvisation so you can audit what your AI did and why.

You need to see when an agent interprets “archive old files” as “delete customer records from 2023.” Before it happens.

Maybe Don’t AI’s MCP gateway sits between your AI agents and your systems. It enforces policies, validates actions, and stops creative interpretations before they become expensive mistakes. It’s the security layer MCP should have launched with.

Your engineers know MCP exists. Now you do too. The question is whether you’ll deploy guardrails before you need them, or after.

Schedule a demo here.